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Alireza Aghasi
The research that I am doing is very computational and requires a lot of processing and memory. I basically deal with Electrical Resistance Tomography (ERT), for detection of contaminants under the surface of the earth. The problem ends up being a very high dimensional Inverse problem which is intensively ill-posed. Dealing with such a problem without appropriate processing power is impossible. Once I became aware of the cluster I started exploring it and realized that some features of it really help me in the processing speed. The excellent feature which really interested me was the good performance in sparse matrix calculations. Star-P does an excellent job dealing with very large sparse systems compared with other platforms. Personally I experienced some very good results using Star-P.
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Umma Rebbapragada
I am a Ph.D. student in computer science, studying machine learning. My research requires me to run experiments in which I test my methods on different data sets. For each data set, I may need to search for or test a particular set of input parameters. For each particular configuration of the experiment, I will need to perform multiple runs in order to ensure my results are statistically significant, or create different samplings of my data. In order to test a wide variety of configurations across multiple data sets, I exploit the cluster's ability to run "embarrassingly parallel" jobs. I have submitted up to 2000 jobs at a time, and have them finish within hours. This has allowed me to test new ideas quickly, and accelerated my overall pace of research. I have different software demands depending on the project I'm working on. These include Java, shell, perl, Matlab, R, C and C++. Fortunately, these are all well-supported on the cluster. I also plan to explore MPI one day and take advantage of products like Star-P, which are available on the cluster.
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